diff --git a/.ipynb_checkpoints/multi-area-model-checkpoint.ipynb b/.ipynb_checkpoints/multi-area-model-checkpoint.ipynb
index 77cddd459e0f51b8386638a22fbea7f490955956..536883ca93042395f7839e6107d8d9ebb4885b95 100644
--- a/.ipynb_checkpoints/multi-area-model-checkpoint.ipynb
+++ b/.ipynb_checkpoints/multi-area-model-checkpoint.ipynb
@@ -10,6 +10,21 @@
"# Down-scaled multi-area model"
]
},
+ {
+ "cell_type": "markdown",
+ "id": "f4a649cc-3b68-49e4-b2b6-6f29f13a6d9c",
+ "metadata": {},
+ "source": [
+ "The code in this notebook implements the down-scaled version of spiking network model of macaque visual cortex developed at the Institute of Neuroscience and Medicine (INM-6), Research Center Jülich. The full-scale model has been documented in the following publications:\n",
+ "\n",
+ " Schmidt M, Bakker R, Hilgetag CC, Diesmann M & van Albada SJ Multi-scale account of the network structure of macaque visual cortex Brain Structure and Function (2018), 223: 1409 https://doi.org/10.1007/s00429-017-1554-4\n",
+ "\n",
+ " Schuecker J, Schmidt M, van Albada SJ, Diesmann M & Helias M (2017) Fundamental Activity Constraints Lead to Specific Interpretations of the Connectome. PLOS Computational Biology, 13(2): e1005179. https://doi.org/10.1371/journal.pcbi.1005179\n",
+ "\n",
+ " Schmidt M, Bakker R, Shen K, Bezgin B, Diesmann M & van Albada SJ (2018) A multi-scale layer-resolved spiking network model of resting-state dynamics in macaque cortex. PLOS Computational Biology, 14(9): e1006359. https://doi.org/10.1371/journal.pcbi.1006359\n",
+ "<br>"
+ ]
+ },
{
"cell_type": "markdown",
"id": "b952d0ea",
@@ -23,7 +38,7 @@
" * [1.1. Parameters to tune](#section_1_1)\n",
" * [1.2. Default parameters](#section_1_2)\n",
"* [S2. Multi-Area Model Instantiation and Simulation](#section_2)\n",
- " * [2.1. Insantiate a multi-area model](#section_2_1)\n",
+ " * [2.1. Instantiate a multi-area model](#section_2_1)\n",
" * [2.2. Predict firing rates from theory](#section_2_2)\n",
" * [2.3. Extract and visualize interareal connectivity](#section_2_3)\n",
" * [2.4. Run a simulation](#section_2_4)\n",
@@ -326,7 +341,7 @@
"tags": []
},
"source": [
- "### 2.1. Insantiate a multi-area model <a class=\"anchor\" id=\"section_2_1\"></a>"
+ "### 2.1. Instantiate a multi-area model <a class=\"anchor\" id=\"section_2_1\"></a>"
]
},
{
@@ -570,22 +585,54 @@
},
{
"cell_type": "code",
- "execution_count": 18,
+ "execution_count": 15,
"id": "ae19bcc3",
"metadata": {
"tags": []
},
"outputs": [
{
- "ename": "TypeError",
- "evalue": "plot_resting_state() missing 1 required positional argument: 'data_path'",
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "loading spikes\n",
+ "Loading data from file\n",
+ "Computing population rates done\n",
+ "Loading data from file\n",
+ "Computing population LvR done\n",
+ "Loading data from file\n",
+ "Loading data from file\n",
+ "Computing rate time series done\n",
+ "Loading data from file\n",
+ "Computing synchrony done\n",
+ "pop_LvR\n",
+ "pop_rates\n",
+ "synchrony\n"
+ ]
+ },
+ {
+ "ename": "FileNotFoundError",
+ "evalue": "[Errno 2] No such file or directory: '/opt/app-root/src/MAM2EBRAINS/simulations/C/Analysis/pop_rates.json'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
- "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
- "Cell \u001b[0;32mIn [18], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mM2E_visualize_resting_state\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m plot_resting_state\n\u001b[0;32m----> 2\u001b[0m plot_resting_state(M, data_path)\n",
- "\u001b[0;31mTypeError\u001b[0m: plot_resting_state() missing 1 required positional argument: 'data_path'"
+ "\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)",
+ "Cell \u001b[0;32mIn [15], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mM2E_visualize_resting_state\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m plot_resting_state\n\u001b[0;32m----> 2\u001b[0m plot_resting_state(M, data_path)\n",
+ "File \u001b[0;32m~/MAM2EBRAINS/./figures/MAM2EBRAINS/M2E_visualize_resting_state.py:197\u001b[0m, in \u001b[0;36mplot_resting_state\u001b[0;34m(M, data_path)\u001b[0m\n\u001b[1;32m 195\u001b[0m \u001b[38;5;66;03m# stationary firing rates\u001b[39;00m\n\u001b[1;32m 196\u001b[0m fn \u001b[38;5;241m=\u001b[39m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin(data_path, label, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mAnalysis\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpop_rates.json\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m--> 197\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mfn\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mr\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mas\u001b[39;00m f:\n\u001b[1;32m 198\u001b[0m pop_rates \u001b[38;5;241m=\u001b[39m json\u001b[38;5;241m.\u001b[39mload(f)\n\u001b[1;32m 200\u001b[0m \u001b[38;5;66;03m# time series of firing rates\u001b[39;00m\n",
+ "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '/opt/app-root/src/MAM2EBRAINS/simulations/C/Analysis/pop_rates.json'"
]
+ },
+ {
+ "data": {
+ "image/png": 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BV5IkSa1k0JUkSVIrGXQlSZLUSgZdSZIktZJBV5IkSa1k0JUkSVIrGXQlSZLUSgZdSZI0Ekm2JnkgyUKSq5bYniQf726/O8lrJlGn2sugK0mSGpdkDXAtsA3YAuxMsqVv2DZgc/exC/jEWItU6xl0JUnSKJwPLFTVkap6EtgPbO8bsx34ZHXcDrwgyUvHXajay6ArSZJGYT3wSM/yYnfd6Y6RVmztpAuQJEmtlCXW1QrGkGQXnVMbAP46yb1D1rZSZwGPzdC8k5z73qr60WHfxKArSZJGYRE4u2d5A3B0BWOoqr3AXoAk81U112ypg5nU3LP6mZt4H09dkCRJo3AI2JxkU5IzgB3Agb4xB4BLu1dfeD3wzar6+rgLVXt5RFeSJDWuqo4nuQK4BVgD7Kuqw0l2d7fvAQ4CFwMLwHeAyyZVr9rJoCtJkkaiqg7SCbO96/b0PC/g8tN8270NlLZSk5rbz7xCnrogSZKmRvd83Zma28+8cgZdSZIktZJBV5IkNSbJy5NU3+MvBnjdim8XvNxrG5j73d05705yW5LzerZ9Lck9Se5cyZUCBpj7wiTf7L7/nUk+NOhrh5z3l3vmvDfJU0l+YNjPnGRfkkdPdom4pvez5+hKkqRR+BPg17rPnzzVwJ7bBV9E55Jjh5IcqKr7eob13i74Ajq3C75gwNcOO/dDwJur6okk2+icP3pBz/a3VNVpX2v2NGr/var66RW+dkXzVtXVwNXd8e8AfrGqHu95mxV9ZuAG4BrgkyfZ3uh+9oiuJEkahWPAl7uPf7/M2GFuFzzIa4eau6puq6onuou307nebxOGqX2cr90JfGrA9z6lqroVePwUQxrdzwZdSZI0Cm+jE3aPAZ9bZuwwtwse9jbCp/v69wE39SwX8MUkd3Tv4HY6Bp37x5PcleSmJOessO6VzEuSZwNbgU/3rB7mM6+0thV9Xk9dkCRJo/CHwD/vPn/iVAMZ7nbBA91GeMi5OwOTt9AJum/sWf2Gqjqa5MXAl5L8afeoZVNz/zHwQ1X1rSQXA5+l82v9YT736bz2HcDv9522MMxnXmltK/q8HtGVJEmj8FhVfbn7uGOZscPcLnig2wgPOTdJzgWuB7ZX1TdOrK+qo92fjwKfofMr9sbmrqq/rKpvdZ8fBJ6V5KxB617pvD120HfawpCfeaW1rejzGnQlSdIovCzJjp7Hs04xdpjbBQ/y2lNZ9vVJNgI3Au+pqgd71j8nyfNOPKdzusaSVxMYYu4fTJLu8/PpZLdvDPm5B3ptkucDb6bn1JMGPvNyGt3PnrogSZJG4cf43iOBLwT+YqmBw9wu+GSvHbTIAef+EPAi4Lpu5jxeVXPAS4DPdNetBX6zqm5ueO53Ab+Q5DjwV8CO7h3lVvy5B5wX4J3AF6vq2z0vH+ozJ/kUcCFwVpJF4FeBZ/XM2+h+TufPavWam5ur+fnTviydpttS5+FMHXt3Jtm7mlat6F2pn6cuSJIkqZUMupIkSWolg64kSWrcMLd6lZpi0JUkSaNwA50bDZxM761ed9G51avUKIOuJElq3BC3epUaY9CVJEmTMOyte6VlGXQlSdIknM6td3clmU8yf84551R3nI92Pxph0JUkSZMw8C1dq2pvVc1V1dyZZ545luLUDgZdSZI0CSe71avUGG8BLEmSGrfSW71KTTLoSpKkxlXVzmW2F3D5mMrRjPLUBUmSJLWSQVeSJEmtZNCVJElSKxl0JUmS1EoGXUmSJLWSQVeSJEmtZNCVJElSKxl0JUmS1EoGXUmSJLVSY0E3ydYkDyRZSHLVScZcmOTOJIeTfLWpuaVh2LuaVvauJJ1aI7cATrIGuBa4CFgEDiU5UFX39Yx5AXAdsLWqHk7y4ibmloZh72pa2buStLymjuieDyxU1ZGqehLYD2zvG3MJcGNVPQxQVY82NLc0DHtX08relaRlNBV01wOP9Cwvdtf1egXwwiRfSXJHkksbmlsahr2raWXvStIyGjl1AcgS62qJuV4LvBU4E/iDJLdX1YMN1SCthL2raWXvStIymjqiuwic3bO8ATi6xJibq+rbVfUYcCtwXkPzSytl72pa2bta9Zb7wmSS5yf5fJK7ul+YvGwSdaq9mgq6h4DNSTYlOQPYARzoG/M54E1J1iZ5NnABcH9D80srZe9qWtm7WtV6vjC5DdgC7EyypW/Y5cB9VXUecCHw0W4/S41o5NSFqjqe5ArgFmANsK+qDifZ3d2+p6ruT3IzcDfwNHB9Vd3bxPzSStm7mlb2rqbAd78wCZDkxBcm7+sZU8DzkgR4LvA4cHzchaq9mjpHl6o6CBzsW7enb/lq4Oqm5pSaYO9qWtm7WuWW+sLkBX1jrqHzm4ijwPOAn6+qp8dTnmaBd0aTJEmjMMgXJt8O3Am8DHg1cE2S73/GGyW7kswnmT927FjTdarFDLqSJGkUBvnC5GV0rvVcVbUAPAS8qv+NqmpvVc1V1dy6detGVrDax6ArSZJGYZAvTD5M5/J3JHkJ8ErgyFirVKs1do6uJEnSCYN8YRL4MHBDknvonOpwZfdSeFIjDLqSJGkklvvCZFUdBd427ro0Ozx1QZIkSa1k0JUkSVIrGXQlSZLUSgZdSZIktZJBV5IkSa1k0JUkSVIrGXQlSZLUSgZdSZIktZJBV5IkSa1k0JUkSVIrGXQlSZLUSgZdSZIktZJBV5IkSa1k0JUkSSORZGuSB5IsJLnqJGMuTHJnksNJvjruGtVuayddgCRJap8ka4BrgYuAReBQkgNVdV/PmBcA1wFbq+rhJC+eSLFqLY/oSpKkUTgfWKiqI1X1JLAf2N435hLgxqp6GKCqHh1zjWo5g64kSRqF9cAjPcuL3XW9XgG8MMlXktyR5NKxVaeZ4KkLkiRpFLLEuupbXgu8FngrcCbwB0lur6oHv+eNkl3ALoCNGzeOoFS1lUd0JUnSKCwCZ/csbwCOLjHm5qr6dlU9BtwKnNf/RlW1t6rmqmpu3bp1IytY7WPQlSRJo3AI2JxkU5IzgB3Agb4xnwPelGRtkmcDFwD3j7lOtZinLkiSpMZV1fEkVwC3AGuAfVV1OMnu7vY9VXV/kpuBu4Gngeur6t7JVa22MehKkqSRqKqDwMG+dXv6lq8Grh5nXZodnrogSZKkVjLoSpIkqZUMupIkSWolg64kSZJayaArSZKkVjLoSpIkqZUMupIkSWolg64kSZJayaArSZKkVjLoSpIkqZUMupIkSWolg64kSZJayaArSZKkVjLoSpIkqZUMupIkaSSSbE3yQJKFJFedYtzrkjyV5F3jrE/tZ9CVJEmNS7IGuBbYBmwBdibZcpJxHwFuGW+FmgUGXUmSNArnAwtVdaSqngT2A9uXGPcB4NPAo+MsTrPBoCtJkkZhPfBIz/Jid913JVkPvBPYc6o3SrIryXyS+WPHjjVeqNrLoCtJkkYhS6yrvuWPAVdW1VOneqOq2ltVc1U1t27duqbq0wxYO+kCJElSKy0CZ/csbwCO9o2ZA/YnATgLuDjJ8ar67FgqVOsZdCVJ0igcAjYn2QT8GbADuKR3QFVtOvE8yQ3AFwy5apJBV5IkNa6qjie5gs7VFNYA+6rqcJLd3e2nPC9XaoJBV5IkjURVHQQO9q1bMuBW1XvHUZNmi19GkyRJUisZdCVJktRKBl1JkiS1kkFXkiRJrWTQlSRJUisZdCVJktRKBl1JkiS1kkFXkiRJrWTQlSRJUisZdCVJktRKBl1JkiS1kkFXkiRJrWTQlSRJUisZdCVJ0kgk2ZrkgSQLSa5aYvu7k9zdfdyW5LxJ1Kn2MuhKkqTGJVkDXAtsA7YAO5Ns6Rv2EPDmqjoX+DCwd7xVqu0MupIkaRTOBxaq6khVPQnsB7b3Dqiq26rqie7i7cCGMdeoljPoSpKkUVgPPNKzvNhddzLvA24aaUWaOWsnXYAkSWqlLLGulhyYvIVO0H3jSbbvAnYBbNy4san6NAMaO6K73AnnPeNel+SpJO9qam5pGPauppW9q1VuETi7Z3kDcLR/UJJzgeuB7VX1jaXeqKr2VtVcVc2tW7duJMWqnRoJugOecH5i3EeAW5qYVxqWvatpZe9qChwCNifZlOQMYAdwoHdAko3AjcB7qurBCdSolmvqiO6yJ5x3fQD4NPBoQ/NKw7J3Na3sXa1qVXUcuILOP7LuB367qg4n2Z1kd3fYh4AXAdcluTPJ/ITKVUs1dY7uUiecX9A7IMl64J3ATwGva2heaVj2rqaVvatVr6oOAgf71u3pef5+4P3jrkuzo6kjuoOccP4x4MqqeqqhOaUm2LuaVvauJC2jqSO6g5xwPgfsTwJwFnBxkuNV9dmGapBWwt7VtLJ3JWkZTQXd755wDvwZnRPOL+kdUFWbTjxPcgPwBf+y1Spg72pa2buStIxGgm5VHU9y4oTzNcC+Eyecd7fvOeUbSBNi72pa2buStLzGbhix3Annfevf29S80rDsXU0re1eSTs1bAEuSJKmVDLqSJElqJYOuJEmSWsmgK0mSpFYy6EqSJKmVDLqSJElqJYOuJEmSWsmgK0mSpFYy6EqSJKmVDLqSJGkkkmxN8kCShSRXLbE9ST7e3X53ktdMok61l0FXkiQ1Lska4FpgG7AF2JlkS9+wbcDm7mMX8ImxFqnWM+hKkqRROB9YqKojVfUksB/Y3jdmO/DJ6rgdeEGSl467ULXX2kkXIEmSWmk98EjP8iJwwQBj1gNf7x2UZBedI74Af53k3mZLHdhZwGMzNO8k5763qn502Dcx6EqSpFHIEutqBWOoqr3AXoAk81U1N3x5p29Sc8/qZ27ifTx1QZIkjcIicHbP8gbg6ArGSCtm0JUkSaNwCNicZFOSM4AdwIG+MQeAS7tXX3g98M2q+nr/G0kr5akLkiSpcVV1PMkVwC3AGmBfVR1Osru7fQ9wELgYWAC+A1w2wFvvHVHJg5jU3H7mFTLoSpKkkaiqg3TCbO+6PT3PC7j8NN9zYqFvUnP7mVfOUxckSZLUSgZdSZLUmCQvT1J9j7+YdF2aTQZdSZI0Cn8C7Ow+/slyg4e5XfByr21g7nd357w7yW1JzuvZ9rUk9yS5cyWXxBpg7guTfLP7/ncm+dCgrx1y3l/umfPeJE8l+YFhP3OSfUkePdm1kJvez56jK0mSRuEY8OXu87851cCe2wVfROeSY4eSHKiq+3qG9d4u+AI6twu+YMDXDjv3Q8Cbq+qJJNvofFGq9+YXb6mq076pwmnU/ntV9dMrfO2K5q2qq4Gru+PfAfxiVT3e8zYr+szADcA1wCdPsr3R/ewRXUmSNApvoxN2jwGfW2bsMLcLHuS1Q81dVbdV1RPdxdvpXO+3CcPUPs7X7gQ+NeB7n1JV3Qo8foohje5ng64kSRqFP6Rz9O0i4JeWGXuyWwEPMmaQ1w47d6/3ATf1LBfwxSR3dG9VfDoGnfvHk9yV5KYk56yw7pXMS5JnA1uBT/esHuYzr7S2FX1eT12QJEmj8FhVfXn5YcBwtwse6DbCQ87dGZi8hU7QfWPP6jdU1dEkLwa+lORPu0ctm5r7j4EfqqpvJbkY+CydX+sP87lP57XvAH6/77SFYT7zSmtb0ef1iK4kSZq0YW4XPOxthAd6fZJzgeuB7VX1jRPrq+po9+ejwGfo/Iq9sbmr6i+r6lvd5weBZyU5a9C6Vzpvjx30nbYw5GdeaW0r+rwGXUmSNGnD3C54kNcONXeSjcCNwHuq6sGe9c9J8rwTz+mcl7zk1QSGmPsHk6T7/Hw62e0bQ37ugV6b5PnAm+k5x7qBz7ycRvezpy5IkqTGVNXXWPrXzKd6zYpvF3yy1zY894eAFwHXdTPn8aqaA14CfKa7bi3wm1V1c8Nzvwv4hSTHgb8CdnTvKLfizz3gvADvBL5YVd/ueflQnznJp4ALgbOSLAK/CjyrZ95G93M6f1ar19zcXM3Pn/Zl6TTdTusvyNXK3p1J9q6mVSt6V+rnqQuSJElqJYOuJEmSWsmgK0mSGjfMrV6lphh0JUnSKNxA50YDJ9N7q9dddG71KjXKoCtJkho3xK1epcYYdCVJ0iQMe+teaVkGXUmSNAmnc+vdXUnmk8yfc8451R3no92PRhh0JUnSJAx8S9eq2ltVc1U1d+aZZ46lOLWDQVeSJE3CyW71KjXGWwBLkqTGrfRWr1KTDLqSJKlxVbVzme0FXD6mcjSjPHVBkiRJrWTQlSRJUisZdCVJktRKBl1JkiS1kkFXkiRJrWTQlSRJUisZdCVJktRKBl1JkiS1kkFXkiRJrWTQlSRJUisZdCVJktRKBl1JkiS1kkFXkiRJrWTQlSRJI5Fka5IHkiwkuWqJ7c9P8vkkdyU5nOSySdSp9jLoSpKkxiVZA1wLbAO2ADuTbOkbdjlwX1WdB1wIfDTJGWMtVK1m0JUkSaNwPrBQVUeq6klgP7C9b0wBz0sS4LnA48Dx8ZapNjPoSpKkUVgPPNKzvNhd1+sa4EeAo8A9wAer6unxlKdZYNCVJEmjkCXWVd/y24E7gZcBrwauSfL9z3ijZFeS+STzx44da7pOtZhBV5IkjcIicHbP8gY6R257XQbcWB0LwEPAq/rfqKr2VtVcVc2tW7duZAWrfQy6kiRpFA4Bm5Ns6n7BbAdwoG/Mw8BbAZK8BHglcGSsVarV1k66AEmS1D5VdTzJFcAtwBpgX1UdTrK7u30P8GHghiT30DnV4cqqemxiRat1DLqSJGkkquogcLBv3Z6e50eBt427Ls0OT12QJElSKxl0JUmS1EoGXUmSJLWSQVeSJEmtZNCVJElSKzUWdJNsTfJAkoUkVy2x/d1J7u4+bktyXlNzS8OwdzWt7F1JOrVGgm6SNcC1wDZgC7AzyZa+YQ8Bb66qc+lcN29vE3NLw7B3Na3sXUlaXlNHdM8HFqrqSFU9CewHtvcOqKrbquqJ7uLtdG4FKE2avatpZe9K0jKaCrrrgUd6lhe7607mfcBNDc0tDcPe1bSydyVpGU3dGS1LrKslByZvofMX7hsbmlsahr2raWXvStIymgq6i8DZPcsbgKP9g5KcC1wPbKuqbzQ0tzQMe1fTyt6VpGU0derCIWBzkk1JzgB2AAd6ByTZCNwIvKeqHmxoXmlY9q6mlb0rScto5IhuVR1PcgVwC7AG2FdVh5Ps7m7fA3wIeBFwXRKA41U118T80krZu5pW9q4kLS9VS57StWrMzc3V/Pz8pMvQeC117uHUsXdnkr2raTU1vWt/zoxGetI7o0mSpJFY7qYm3TEXJrkzyeEkXx13jWq3pr6MJkmS9F09NzW5iM6XJw8lOVBV9/WMeQFwHbC1qh5O8uKJFKvW8oiuJEkahWVvagJcAtxYVQ8DVNWjY65RLWfQlSRJozDITU1eAbwwyVeS3JHk0rFVp5ngqQuSJGkUBrmpyVrgtcBbgTOBP0hye//l8JLsAnYBbNy4cQSlqq08oitJkkZhkJuaLAI3V9W3q+ox4FbgvP43qqq9VTVXVXPr1q0bWcFqH4OuJEkahWVvagJ8DnhTkrVJng1cANw/5jrVYp66IEmSGjfITU2q6v4kNwN3A08D11fVvZOrWm1j0JUkSSNRVQeBg33r9vQtXw1cPc66NDs8dUGSJEmtZNCVJElSKxl0JUmS1EoGXUmSJLWSQVeSJEmtZNCVJElSKxl0JUmS1EoGXUmSJLWSQVeSJEmtZNCVJElSKxl0JUmS1EoGXUmSJLWSQVeSJEmtZNCVJEkjkWRrkgeSLCS56hTjXpfkqSTvGmd9aj+DriRJalySNcC1wDZgC7AzyZaTjPsIcMt4K9QsMOhKkqRROB9YqKojVfUksB/YvsS4DwCfBh4dZ3GaDQZdSZI0CuuBR3qWF7vrvivJeuCdwJ4x1qUZYtCVJEmjkCXWVd/yx4Arq+qpU75RsivJfJL5Y8eONVWfZsDaSRcgSZJaaRE4u2d5A3C0b8wcsD8JwFnAxUmOV9VnewdV1V5gL8Dc3Fx/WJZOyqArSZJG4RCwOckm4M+AHcAlvQOqatOJ50luAL7QH3KlYRh0JUlS46rqeJIr6FxNYQ2wr6oOJ9nd3e55uRo5g64kSRqJqjoIHOxbt2TArar3jqMmzRa/jCZJkqRWMuhKkiSplQy6kiRJaiWDriRJklrJoCtJkqRWMuhKkiSplQy6kiRJaiWDriRJklrJoCtJkqRWMuhKkiSplQy6kiRJaiWDriRJklrJoCtJkqRWMuhKkiSplQy6kiRpJJJsTfJAkoUkVy2x/d1J7u4+bkty3iTqVHsZdCVJUuOSrAGuBbYBW4CdSbb0DXsIeHNVnQt8GNg73irVdgZdSZI0CucDC1V1pKqeBPYD23sHVNVtVfVEd/F2YMOYa1TLGXQlSdIorAce6Vle7K47mfcBN420Is2ctZMuQJIktVKWWFdLDkzeQifovvEk23cBuwA2btzYVH2aAR7RlSRJo7AInN2zvAE42j8oybnA9cD2qvrGUm9UVXuraq6q5tatWzeSYtVOBl1JkjQKh4DNSTYlOQPYARzoHZBkI3Aj8J6qenACNarlPHVBkiQ1rqqOJ7kCuAVYA+yrqsNJdne37wE+BLwIuC4JwPGqmptUzWofg64kSRqJqjoIHOxbt6fn+fuB94+7Ls0OT12QJElSKxl0JUmS1EoGXUmSJLWSQVeSJEmtZNCVJElSKxl0JUmS1EoGXUmSJLWSQVeSJEmtZNCVJElSKxl0JUmS1EoGXUmSJLWSQVeSJEmtZNCVJElSKzUWdJNsTfJAkoUkVy2xPUk+3t1+d5LXNDW3NAx7V9PK3tVqZ49q0hoJuknWANcC24AtwM4kW/qGbQM2dx+7gE80Mbc0DHtX08re1Wpnj2o1aOqI7vnAQlUdqaongf3A9r4x24FPVsftwAuSvLSh+aWVsnc1rexdrXb2qCauqaC7HnikZ3mxu+50x0jjZu9qWtm7Wu3sUU3c2obeJ0usqxWMeYY77rjjW0keWFFVwzsLeMy5x+7eqvrRMc1l7zp3k+zd4c1y/0xy/lH0bmM9mmQXnVMbAP46yb1D1rZSk9pHs/jfRSM92VTQXQTO7lneABxdwZilPFBVc8OVtzJJ5p17MvOPcTp717kbnX+M09m7LZp70vOPqHcb69Gq2gvshcn/OU1i7ln9zE28T1OnLhwCNifZlOQMYAdwoG/MAeDS7jcsXw98s6q+3tD80krZu5pW9q5WO3tUE9fIEd2qOp7kCuAWYA2wr6oOJ9nd3b4HOAhcDCwA3wEua2JuaRj2rqaVvavVzh7VatDUqQtU1UE6Ddu7bk/P8wIuX8Fb7x2ytGE49wzMb+8697TOb++2au5Jzz+SuUfUo637c1rF805y7kbmTafHJEmSpHbxFsCSJElqpYkF3WFuC7jcaxuY+93dOe9OcluS83q2fS3JPUnuXOk3AgeY/8Ik3+zOcWeSDw362gbm/uWeee9N8lSSH+huG+qzJ9mX5NGTXRZmlPu8SfauvbvEdnt3+Lnt3Rnv3Vntz0n15qT6cuw9WVVjf9A5Kf0/AT8MnAHcBWzpG3MxcBOda+y9HvjDQV/bwNw/Abyw+3zbibm7y18DzhrxZ78Q+MJKXjvs3H3j3wH8boOf/SeB19C5Nt5S20eyz+1de9fetXft3dXdu7Pan5PqzUn25bh7clJHdIe5LeAgrx1q7qq6raqe6C7eTue6fk0Zpv6Rf/Y+O4FPncb7n1JV3Qo8fooho9rnTbJ37d2l2LtDzm3vftes9u6s9uekenNifTnunpxU0B3mtoDD3i7wdF//Pjr/sjihgC8muSOdO7WcrkHn//EkdyW5Kck5p/naYecmybOBrcCne1YP+9lXWt9qukWkvbv8/PauvbuSuXvZu39rVnp3VvtzUr25mvuy0f3c2OXFTtMwtwVc0S0tT3PuzsDkLXQa+o09q99QVUeTvBj4UpI/7f7rpMn5/xj4oar6VpKLgc8Cm0+n9iHmPuEdwO9XVe+/uob97Cutb9jP3SR799Tz27vfW5+9O/jcnYH27qz27qz256R6czX3ZaP7eVJHdIe5LeBKb2l5OnOT5FzgemB7VX3jxPqqOtr9+SjwGTqH0k/HsvNX1V9W1be6zw8Cz0py1qC1DzN3jx30/Zqigc++0vqG/dxNsndPMb+9+4z67N3B57Z3Z7t3Z7U/J9Wbq7kvm93PNaITy0/1oHMk+Qiwib89oficvjH/kO89GfmPBn1tA3NvpHOXlp/oW/8c4Hk9z28Dto7gs/8gf3uN4/OBh7t/DiP/7N1xz6dz/sxzmvzs3de+nJOfgD6SfW7v2rv2rr1r767u3p3V/pxUb066L8fZkyNp2AE/5MXAg3S+Qfcr3XW7gd3d5wGu7W6/B5g71Wsbnvt64Angzu5jvrv+h7t/sHcBh1cy94DzX9F9/7vonPT+E+P67N3l9wL7+1439Gen8y/CrwN/Q+dfZu8b1z63d+1de9fetXdXd+/Oan9Oqjcn1Zfj7knvjCZJkqRW8s5okiRJaiWDriRJklrJoCtJkqRWMuhKkiSplQy6kiRJaiWDriRJklrJoCtJkqRWMuhKkiSplQy6kiRJaiWDriRJklrJoCtJkqRWMuhKkiSplaY66CbZl+TRJPeeZHuSfDzJQpK7k7xm3DVKkiRpMqY66AI3AFtPsX0bsLn72AV8Ygw1SZIkaRWY6qBbVbcCj59iyHbgk9VxO/CCJC8dT3WSJEmapKkOugNYDzzSs7zYXSdJkqSWWzvpAkYsS6yrZwxKdtE5tYHnPOc5r33Vq1416rokSZJ0EnfcccdjVbVu2PcZKOgmeQ7wr4B3AS8FvgH8EfCBqnp42CJGaBE4u2d5A3C0f1BV7QX2AszNzdX8/Px4qpMkSdIzJPnPTbzPsqcuJAnwO8D/ABwB/jvg/wB+CNjYRBEjdAC4tHv1hdcD36yqr0+6KEmSJI3eIEd0fwp4M3A/8A+q6imAJL8G/J0R1rasJJ8CLgTOSrII/CrwLICq2gMcBC4GFoDvAJdNplJJkiSN2yBB97Xdn1+sqqeS/F3gud113xlNWYOpqp3LbC/g8jGVI0mSpFXkdK66cOJLXLuBY93HP2u8IkmSJKkBgwTdE9/Memv3fN1P0/limiRJkrRqDRJ0/wPwFeDvAzcBb6Nz5QVJkiRp1Vr2HN2qqiTvAD4M/BydL6f9OfDv6FyNQZIkSVp1BrqOblV9C/jF7kOSJEla9dp+C2BJkiTNKIOuJEmSWsmgK0mSpFYy6EqSJKmVDLqSJElqJYOuJEmSWsmgK0mSpFYy6EqSJKmVDLqSJElqJYOuJEmSWsmgK0mSpFYy6EqSJKmVDLqSJElqJYOuJEmSWsmgK0mSpFaa6qCbZGuSB5IsJLlqie3PT/L5JHclOZzksknUKUmSpPGb2qCbZA1wLbAN2ALsTLKlb9jlwH1VdR5wIfDRJGeMtVBJkiRNxNQGXeB8YKGqjlTVk8B+YHvfmAKelyTAc4HHgePjLVOSJEmTMM1Bdz3wSM/yYnddr2uAHwGOAvcAH6yqp/vfKMmuJPNJ5o8dOzaqeiVJkjRG0xx0s8S66lt+O3An8DLg1cA1Sb7/GS+q2ltVc1U1t27duqbrlCRJ0gRMc9BdBM7uWd5A58htr8uAG6tjAXgIeNWY6pMkSdIETXPQPQRsTrKp+wWzHcCBvjEPA28FSPIS4JXAkbFWKUmSpIlYO+kCVqqqjie5ArgFWAPsq6rDSXZ3t+8BPgzckOQeOqc6XFlVj02saEmSJI3N1AZdgKo6CBzsW7en5/lR4G3jrkuSJEmTN82nLkiSJEknZdCVJElSKxl0JUmS1EoGXUmSJLWSQVeSJEmtZNCVJElSKxl0JUmS1EoGXUmSJLWSQVeSJEmtZNCVJElSKxl0JUmS1EoGXUmSJLWSQVeSJEmtZNCVJElSKxl0JUmS1EoGXUmSJLWSQVeSJEmtZNCVJElSKxl0JUmS1EpTHXSTbE3yQJKFJFedZMyFSe5McjjJV8ddoyRJkiZj7aQLWKkka4BrgYuAReBQkgNVdV/PmBcA1wFbq+rhJC+eSLGSJEkau2k+ons+sFBVR6rqSWA/sL1vzCXAjVX1MEBVPTrmGiVJkjQh0xx01wOP9Cwvdtf1egXwwiRfSXJHkkvHVp0kSZImampPXQCyxLrqW14LvBZ4K3Am8AdJbq+qB7/njZJdwC6AjRs3jqBUSZIkjds0H9FdBM7uWd4AHF1izM1V9e2qegy4FTiv/42qam9VzVXV3Lp160ZWsCRJksZnmoPuIWBzkk1JzgB2AAf6xnwOeFOStUmeDVwA3D/mOiVJkjQBU3vqQlUdT3IFcAuwBthXVYeT7O5u31NV9ye5GbgbeBq4vqrunVzVkiRJGpdU9Z/WOtvm5uZqfn5+0mVIkiTNrCR3VNXcsO8zzacuSJIkSSdl0JUkSVIrGXQlSZLUSgZdSZIktZJBV5IkSa1k0JUkSVIrGXQlSZLUSgZdSZIktZJBV5IkSa1k0JUkSVIrGXQlSZLUSgZdSZIktZJBV5IkSa1k0JUkSVIrGXQlSZLUSgZdSZIktZJBV5IkSa1k0JUkSVIrGXQlSZLUSgZdSZIktdJUB90kW5M8kGQhyVWnGPe6JE8ledc465MkSdLkTG3QTbIGuBbYBmwBdibZcpJxHwFuGW+FkiRJmqSpDbrA+cBCVR2pqieB/cD2JcZ9APg08Og4i5MkSdJkTXPQXQ880rO82F33XUnWA+8E9oyxLkmSJK0C0xx0s8S66lv+GHBlVT11yjdKdiWZTzJ/7NixpuqTJEnSBK2ddAFDWATO7lneABztGzMH7E8CcBZwcZLjVfXZ3kFVtRfYCzA3N9cfliVJkjSFpjnoHgI2J9kE/BmwA7ikd0BVbTrxPMkNwBf6Q64kSZLaaWqDblUdT3IFnasprAH2VdXhJLu72z0vV5IkaYZNbdAFqKqDwMG+dUsG3Kp67zhqkiRJ0uowzV9GkyRJkk7KoCtJkqRWMuhKkiSplQy6kiRJaiWDriRJklrJoCtJkqRWMuhKkiSplQy6kiRJaiWDriRJklrJoCtJkqRWMuhKkiSplQy6kiRJaiWDriRJklrJoCtJkqRWMuhKkiSplQy6kiRJaiWDriRJklrJoCtJkqRWMuhKkiSplaY66CbZmuSBJAtJrlpi+7uT3N193JbkvEnUKUmSpPGb2qCbZA1wLbAN2ALsTLKlb9hDwJur6lzgw8De8VYpSZKkSZnaoAucDyxU1ZGqehLYD2zvHVBVt1XVE93F24ENY65RkiRJEzLNQXc98EjP8mJ33cm8D7hppBVJkiRp1Vg76QKGkCXW1ZIDk7fQCbpvPMn2XcAugI0bNzZVnyRJkiZomo/oLgJn9yxvAI72D0pyLnA9sL2qvrHUG1XV3qqaq6q5devWjaRYSZIkjdc0B91DwOYkm5KcAewADvQOSLIRuBF4T1U9OIEaJUmSNCFTe+pCVR1PcgVwC7AG2FdVh5Ps7m7fA3wIeBFwXRKA41U1N6maJUmSND6pWvK01pk1NzdX8/Pzky5DkiRpZiW5o4mDk9N86oIkSZJ0UgZdSZIktZJBV5IkSa1k0JUkSVIrGXQlSZLUSgZdSZIktZJBV5IkSa1k0JUkSVIrGXQlSZLUSgZdSZIktZJBV5IkSa1k0JUkSVIrGXQlSZLUSgZdSZIktZJBV5IkSa1k0JUkSVIrGXQlSZLUSgZdSZIktZJBV5IkSa1k0JUkSVIrTXXQTbI1yQNJFpJctcT2JPl4d/vdSV4ziTolSZI0flMbdJOsAa4FtgFbgJ1JtvQN2wZs7j52AZ8Ya5GSJEmamKkNusD5wEJVHamqJ4H9wPa+MduBT1bH7cALkrx03IVKkiRp/KY56K4HHulZXuyuO90xkiRJaqG1ky5gCFliXa1gDEl20Tm1AeCvk9w7ZG2aDmcBj026CI2c+3l2uK9ng/t5NryyiTeZ5qC7CJzds7wBOLqCMVTVXmAvQJL5qpprtlStRu7r2eB+nh3u69ngfp4NSeabeJ9pPnXhELA5yaYkZwA7gAN9Yw4Al3avvvB64JtV9fVxFypJkqTxm9ojulV1PMkVwC3AGmBfVR1Osru7fQ9wELgYWAC+A1w2qXolSZI0XlMbdAGq6iCdMNu7bk/P8wIuP8233dtAaZoO7uvZ4H6eHe7r2eB+ng2N7Od0sqAkSZLULtN8jq4kSZJ0UjMbdL198GwYYD+/u7t/705yW5LzJlGnhrfcvu4Z97okTyV51zjrUzMG2c9JLkxyZ5LDSb467hrVjAH+/n5+ks8nuau7r/0ezhRKsi/Joye7tOuweWwmg663D54NA+7nh4A3V9W5wIfx3K+pNOC+PjHuI3S+xKopM8h+TvIC4DrgZ6rqHODnxl2nhjfgf9OXA/dV1XnAhcBHu1dh0nS5Adh6iu1D5bGZDLp4++BZsex+rqrbquqJ7uLtdK61rOkzyH/TAB8APg08Os7i1JhB9vMlwI1V9TBAVbmvp9Mg+7qA5yUJ8FzgceD4eMvUsKrqVjr77mSGymOzGnS9ffBsON19+D7gppFWpFFZdl8nWQ+8E9iDptUg/02/Anhhkq8kuSPJpWOrTk0aZF9fA/wInRtB3QN8sKqeHk95GqOh8thUX15sCI3dPlir2sD7MMlb6ATdN460Io3KIPv6Y8CVVfVU5wCQptAg+3kt8FrgrcCZwB8kub2qHhx1cWrUIPv67cCdwE8Bfw/4UpLfq6q/HHFtGq+h8tisBt3Gbh+sVW2gfZjkXOB6YFtVfWNMtalZg+zrOWB/N+SeBVyc5HhVfXYsFaoJg/7d/VhVfRv4dpJbgfMAg+50GWRfXwb8L91r5i8keQh4FfBH4ylRYzJUHpvVUxe8ffBsWHY/J9kI3Ai8xyM+U23ZfV1Vm6rq5VX1cuDfAf/UkDt1Bvm7+3PAm5KsTfJs4ALg/jHXqeENsq8fpnPkniQvAV4JHBlrlRqHofLYTB7R9fbBs2HA/fwh4EXAdd0jfceram5SNWtlBtzXmnKD7Oequj/JzcDdwNPA9VW15GWLtHoN+N/0h4EbktxD59fbV1bVYxMrWiuS5FN0rppxVpJF4FeBZ0Ezecw7o0mSJKmVZvXUBUmSJLWcQVeSJEmtZNCVJElSKxl0JUmS1EoGXUmSJLWSQVeSJEmtZNCVJElSKxl0JUmS1Er/PwIZ1L0TrIVtAAAAAElFTkSuQmCC\n",
+ "text/plain": [
+ "<Figure size 720x635.692 with 7 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
}
],
"source": [
@@ -606,22 +653,10 @@
},
{
"cell_type": "code",
- "execution_count": 19,
+ "execution_count": null,
"id": "721d1f03-df25-468d-8075-a807025a9c58",
"metadata": {},
- "outputs": [
- {
- "ename": "NameError",
- "evalue": "name 'A' is not defined",
- "output_type": "error",
- "traceback": [
- "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
- "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
- "Cell \u001b[0;32mIn [19], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# %%capture captured\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m A\u001b[38;5;241m.\u001b[39mshow_rates()\n",
- "\u001b[0;31mNameError\u001b[0m: name 'A' is not defined"
- ]
- }
- ],
+ "outputs": [],
"source": [
"# %%capture captured\n",
"A.show_rates()"
@@ -629,19 +664,10 @@
},
{
"cell_type": "code",
- "execution_count": 20,
+ "execution_count": null,
"id": "5b40db5d-51b2-4d16-b36b-9f1995452b05",
"metadata": {},
- "outputs": [
- {
- "ename": "SyntaxError",
- "evalue": "invalid syntax (2792692880.py, line 1)",
- "output_type": "error",
- "traceback": [
- "\u001b[0;36m Cell \u001b[0;32mIn [20], line 1\u001b[0;36m\u001b[0m\n\u001b[0;31m |Index|0|1|2|3|4|5|6|7|\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"|Index|0|1|2|3|4|5|6|7|\n",
"|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|\n",
diff --git a/figures/MAM2EBRAINS/.ipynb_checkpoints/M2E_visualize_resting_state-checkpoint.py b/figures/MAM2EBRAINS/.ipynb_checkpoints/M2E_visualize_resting_state-checkpoint.py
index 842430178a5e510ce6b60c0a6dd3a15fccf30e79..72be35705bec5c5c9378e97f6f61d2761555b955 100644
--- a/figures/MAM2EBRAINS/.ipynb_checkpoints/M2E_visualize_resting_state-checkpoint.py
+++ b/figures/MAM2EBRAINS/.ipynb_checkpoints/M2E_visualize_resting_state-checkpoint.py
@@ -193,7 +193,7 @@ def plot_resting_state(M, data_path):
spike_data = A.spike_data
# stationary firing rates
- fn = os.path.join(data_path, label, 'Analysis', 'pop_rates.json')
+ fn = os.path.join(data_path, str(label), 'Analysis', 'pop_rates.json')
with open(fn, 'r') as f:
pop_rates = json.load(f)
diff --git a/figures/MAM2EBRAINS/M2E_visualize_resting_state.py b/figures/MAM2EBRAINS/M2E_visualize_resting_state.py
index 842430178a5e510ce6b60c0a6dd3a15fccf30e79..72be35705bec5c5c9378e97f6f61d2761555b955 100644
--- a/figures/MAM2EBRAINS/M2E_visualize_resting_state.py
+++ b/figures/MAM2EBRAINS/M2E_visualize_resting_state.py
@@ -193,7 +193,7 @@ def plot_resting_state(M, data_path):
spike_data = A.spike_data
# stationary firing rates
- fn = os.path.join(data_path, label, 'Analysis', 'pop_rates.json')
+ fn = os.path.join(data_path, str(label), 'Analysis', 'pop_rates.json')
with open(fn, 'r') as f:
pop_rates = json.load(f)
diff --git a/multi-area-model.ipynb b/multi-area-model.ipynb
index 10a1d0fc8e8f834aaef0a7d7704bd9ed1c8662c7..536883ca93042395f7839e6107d8d9ebb4885b95 100644
--- a/multi-area-model.ipynb
+++ b/multi-area-model.ipynb
@@ -10,6 +10,21 @@
"# Down-scaled multi-area model"
]
},
+ {
+ "cell_type": "markdown",
+ "id": "f4a649cc-3b68-49e4-b2b6-6f29f13a6d9c",
+ "metadata": {},
+ "source": [
+ "The code in this notebook implements the down-scaled version of spiking network model of macaque visual cortex developed at the Institute of Neuroscience and Medicine (INM-6), Research Center Jülich. The full-scale model has been documented in the following publications:\n",
+ "\n",
+ " Schmidt M, Bakker R, Hilgetag CC, Diesmann M & van Albada SJ Multi-scale account of the network structure of macaque visual cortex Brain Structure and Function (2018), 223: 1409 https://doi.org/10.1007/s00429-017-1554-4\n",
+ "\n",
+ " Schuecker J, Schmidt M, van Albada SJ, Diesmann M & Helias M (2017) Fundamental Activity Constraints Lead to Specific Interpretations of the Connectome. PLOS Computational Biology, 13(2): e1005179. https://doi.org/10.1371/journal.pcbi.1005179\n",
+ "\n",
+ " Schmidt M, Bakker R, Shen K, Bezgin B, Diesmann M & van Albada SJ (2018) A multi-scale layer-resolved spiking network model of resting-state dynamics in macaque cortex. PLOS Computational Biology, 14(9): e1006359. https://doi.org/10.1371/journal.pcbi.1006359\n",
+ "<br>"
+ ]
+ },
{
"cell_type": "markdown",
"id": "b952d0ea",
@@ -23,7 +38,7 @@
" * [1.1. Parameters to tune](#section_1_1)\n",
" * [1.2. Default parameters](#section_1_2)\n",
"* [S2. Multi-Area Model Instantiation and Simulation](#section_2)\n",
- " * [2.1. Insantiate a multi-area model](#section_2_1)\n",
+ " * [2.1. Instantiate a multi-area model](#section_2_1)\n",
" * [2.2. Predict firing rates from theory](#section_2_2)\n",
" * [2.3. Extract and visualize interareal connectivity](#section_2_3)\n",
" * [2.4. Run a simulation](#section_2_4)\n",
@@ -326,7 +341,7 @@
"tags": []
},
"source": [
- "### 2.1. Insantiate a multi-area model <a class=\"anchor\" id=\"section_2_1\"></a>"
+ "### 2.1. Instantiate a multi-area model <a class=\"anchor\" id=\"section_2_1\"></a>"
]
},
{